Noise detection and suppression of oil well pumping units pose challenges in data processing for mature exploration areas.The conventional method in the industry is to identify pumping unit noise through manual interactions and then suppress it as high-amplitude interference.However,manual identification wastes manpower and yields low detection accuracy,often resulting in missed detections.Hence,based on the noise characteristics of pumping units,this study conducted noise detection on seismic data containing pumping unit noise using deep learning methods.It then estimated the bandwidth of the detected noise using mathematical morphology techniques to determine the final position and distribution pattern of the noise.This allows for adaptive parameter support for the anomalous amplitude attenuation(AAA) method to achieve automatic detection and efficient suppression of pumping unit noise.The processing results of actual seismic data reveal that the methodology used in this study enables intelligent detection of pumping unit noise,significantly reducing the manual effort required for noise identification,improving the detection accuracy,and enhancing the fidelity and robustness of the AAA method.
张猛. 地震资料处理中油井抽油机噪声干扰智能检测与压制方法[J]. 物探与化探, 2025, 49(2): 378-384.
ZHANG Meng. Intelligent detection and suppression methodology for noise interference of oil well pumping units in seismic data processing. Geophysical and Geochemical Exploration, 2025, 49(2): 378-384.
Wu L L. Three-dimensional vector combination method for suppressing the noise of pumping unit[J]. Petroleum Geology and Recovery Efficiency, 2010, 17(6):51-53,114.
Wang X. Suppressing the noise from pumping unit based on 3-D vector combination of geophones[J]. Geophysical Prospecting for Petroleum, 2011, 50(3):295-300,306,7.
Zhao Y Q, Xiao Y S. Method for noise suppression of pumping units based on blind source separation[J]. Journal of Computer Applications, 2014, 34(S1):349-351.
Li X L, Wang X K, Zhao H, et al. Well-pump noise attenuating method based on morphological component analysis[J]. Progress in Geophysics, 2017, 32(2):657-663.
[5]
Liu D W, Wang W, Wang X K, et al. Poststack seismic data denoising based on 3-D convolutional neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(3):1598-1629.
[6]
Yuan Y J, Si X, Zheng Y. Ground-roll attenuation using generative adversarial networks[J]. Geophysics, 2020, 85(4):WA255-WA267.
[7]
Kaur H, Fomel S, Pham N. Seismic ground-roll noise attenuation using deep learning[J]. Geophysical Prospecting, 2020, 68(7):2064-2077.
Zhang M, Xu Y Z, Yi J D. A method for seismic data denoising based on the neural network with a retractable attention mechanism[J]. Geophysical and Geochemical Exploration, 2024, 48(4):1065-1075.
Zhou H, Sun C Y, Liu Y C, et al. A method for strong noise suppression based on DC-UNet[J]. Geophysical and Geochemical Exploration, 2023, 47(5):1288-1297.
[10]
Sun J, Slang S, Elboth T, et al. A convolutional neural network approach to deblending seismic data[J]. Geophysics, 2020, 85(4):WA13-WA26.
[11]
Zhao Y X, Li Y, Dong X T, et al. Low-frequency noise suppression method based on improved DnCNN in desert seismic data[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(5):811-815.
[12]
Yu S, Ma J, Wang W. Deep learning for denoising[J]. Geophysics, 2019, 84(6):V333-V350.
doi: 10.1190/GEO2018-0668.1
[13]
Ren H P, Li C, Wen X T, et al. Suppressing well-pump noise from seismic data based on multilayer generator network[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:8028705.
[14]
Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.
[15]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. Computer Science, 2014.
